Build and Deploy Machine Learning Solutions on Vertex AI
Earned oct. 30, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned oct. 10, 2024 EDT
Natural Language Processing on Google Cloud
Earned sept. 30, 2024 EDT
Production Machine Learning Systems
Earned sept. 2, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned août 20, 2024 EDT
SOAR Fundamentals
Earned août 15, 2024 EDT
Feature Engineering
Earned juil. 30, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned juil. 30, 2024 EDT
Unlocking the Power of Google Cloud Generative AI for Partners
Earned juil. 16, 2024 EDT
Google Cloud Generative AI Trailblazer
Earned juil. 16, 2024 EDT
Launching into Machine Learning
Earned juil. 16, 2024 EDT
Introduction to CES and Conversational Agents
Earned juil. 5, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned juin 28, 2024 EDT
Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI course, where you will learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models. This skill badge course is for professional Data Scientists and Machine Learning Engineers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
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This course will familiarize you with the core functionality of Chronicle, including the user interface, connections, and settings.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases.
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This course is for Google Cloud’s top partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases. Those who complete the training and assessment will receive the Google Cloud Generative AI Trailblazer badge through Skills Boost.
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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course explores the different products and capabilities of Customer Engagement Suite (CES) and Conversational agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
Text Prompt Engineering Techniques
Earned déc. 4, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned déc. 3, 2024 EST
Machine Learning Operations (MLOps): Getting Started
Earned oct. 30, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned oct. 30, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned oct. 10, 2024 EDT
Natural Language Processing on Google Cloud
Earned sept. 30, 2024 EDT
Production Machine Learning Systems
Earned sept. 2, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned août 20, 2024 EDT
SOAR Fundamentals
Earned août 15, 2024 EDT
Feature Engineering
Earned juil. 30, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned juil. 30, 2024 EDT
Unlocking the Power of Google Cloud Generative AI for Partners
Earned juil. 16, 2024 EDT
Google Cloud Generative AI Trailblazer
Earned juil. 16, 2024 EDT
Launching into Machine Learning
Earned juil. 16, 2024 EDT
Introduction to CES and Conversational Agents
Earned juil. 5, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned juin 28, 2024 EDT